






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def        UpperCAmelCase__		(						lowerCamelCase_       :       BertModel   ,  lowerCamelCase_       :       str   ,  lowerCamelCase_       :       str				):
			__a						:  str									=     ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

			__a						:  int									=     (
			    ('layer.', 'layer_'),
			    ('word_embeddings.weight', 'word_embeddings'),
			    ('position_embeddings.weight', 'position_embeddings'),
			    ('token_type_embeddings.weight', 'token_type_embeddings'),
			    ('.', '/'),
			    ('LayerNorm/weight', 'LayerNorm/gamma'),
			    ('LayerNorm/bias', 'LayerNorm/beta'),
			    ('weight', 'kernel'),
			)

			if not os.path.isdir(lowerCamelCase_				):
						os.makedirs(lowerCamelCase_				)

			__a						:  Union[str, Any]									=     model.state_dict()

			def to_tf_var_name(lowerCamelCase_       :       str				):
						for patt, repl in iter(lowerCamelCase_				):
									__a						:  Union[str, Any]									=     name.replace(lowerCamelCase_   ,  lowerCamelCase_				)
						return f'''bert/{name}'''

			def create_tf_var(lowerCamelCase_       :       np.ndarray   ,  lowerCamelCase_       :       str   ,  lowerCamelCase_       :       tf.Session				):
						__a						:  List[Any]									=     tf.dtypes.as_dtype(tensor.dtype				)
						__a						:  List[str]									=     tf.get_variable(dtype=lowerCamelCase_   ,  shape=tensor.shape   ,  name=lowerCamelCase_   ,  initializer=tf.zeros_initializer()				)
						session.run(tf.variables_initializer([tf_var]				)				)
						session.run(lowerCamelCase_				)
						return tf_var

			tf.reset_default_graph()
			with tf.Session() as session:
						for var_name in state_dict:
									__a						:  Optional[Any]									=     to_tf_var_name(lowerCamelCase_				)
									__a						:  Optional[Any]									=     state_dict[var_name].numpy()
									if any(x in var_name for x in tensors_to_transpose				):
												__a						:  Union[str, Any]									=     torch_tensor.T
									__a						:  str									=     create_tf_var(tensor=lowerCamelCase_   ,  name=lowerCamelCase_   ,  session=lowerCamelCase_				)
									tf.keras.backend.set_value(lowerCamelCase_   ,  lowerCamelCase_				)
									__a						:  Union[str, Any]									=     session.run(lowerCamelCase_				)
									print(f'''Successfully created {tf_name}: {np.allclose(lowerCamelCase_   ,  lowerCamelCase_				)}'''				)

						__a						:  Any									=     tf.train.Saver(tf.trainable_variables()				)
						saver.save(lowerCamelCase_   ,  os.path.join(lowerCamelCase_   ,  model_name.replace('-'   ,  '_'				) + '.ckpt'				)				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Union[str, Any]=None				):
			__a						:  str									=     argparse.ArgumentParser()
			parser.add_argument('--model_name'   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='model name e.g. bert-base-uncased'				)
			parser.add_argument(
			    '--cache_dir'   ,  type=lowerCamelCase_   ,  default=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='Directory containing pytorch model'				)
			parser.add_argument('--pytorch_model_path'   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='/path/to/<pytorch-model-name>.bin'				)
			parser.add_argument('--tf_cache_dir'   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='Directory in which to save tensorflow model'				)
			__a						:  List[Any]									=     parser.parse_args(lowerCamelCase_				)

			__a						:  Optional[Any]									=     BertModel.from_pretrained(
			    pretrained_model_name_or_path=args.model_name   ,  state_dict=torch.load(args.pytorch_model_path				)   ,  cache_dir=args.cache_dir   ,  )

			convert_pytorch_checkpoint_to_tf(model=lowerCamelCase_   ,  ckpt_dir=args.tf_cache_dir   ,  model_name=args.model_name				)


if __name__ == "__main__":
							main()











'''simple docstring'''







def 						_A      ( UpperCAmelCase = 1      ,UpperCAmelCase = 1000						):






   '''simple docstring'''







   A__       =     1
   A__       =     0

   for divide_by_number in range(UpperCAmelCase      ,digit + 1						):
      A__       =     []
      A__       =     numerator
      for _ in range(1      ,digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(UpperCAmelCase						):
               A__       =     len(UpperCAmelCase						)
               A__       =     divide_by_number
         else:
            has_been_divided.append(UpperCAmelCase						)
            A__       =     now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


